Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

doi:10.1007/s11705-017-1675-6

Front. Chem. Sci. Eng.

2017, Vol. 11

Issue (3) : 429-439 https://doi.org/10.1007/s11705-017-1675-6

RESEARCH ARTICLE

Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection

Xin Peng, Yang Tang, Wenli Du(

), Feng Qian(

)

Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

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Abstract

In this paper, we propose a novel performance monitoring and fault detection method, which is based on modified structure analysis and globality and locality preserving (MSAGL) projection, for non-Gaussian processes with multiple operation conditions. By using locality preserving projection to analyze the embedding geometrical manifold and extracting the non-Gaussian features by independent component analysis, MSAGL preserves both the global and local structures of the data simultaneously. Furthermore, the tradeoff parameter of MSAGL is tuned adaptively in order to find the projection direction optimal for revealing the hidden structural information. The validity and effectiveness of this approach are illustrated by applying the proposed technique to the Tennessee Eastman process simulation under multiple operation conditions. The results demonstrate the advantages of the proposed method over conventional eigendecomposition-based monitoring methods.

Keywords non-Gaussian processes subspace projection independent component analysis locality preserving projection finite mixture model

Corresponding Author(s): Wenli Du,Feng Qian

Just Accepted Date: 07 July 2017 Online First Date: 11 August 2017 Issue Date: 23 August 2017

Cite this article:

Xin Peng,Yang Tang,Wenli Du, et al. Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection[J]. Front. Chem. Sci. Eng., 2017, 11(3): 429-439.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-017-1675-6
https://academic.hep.com.cn/fcse/EN/Y2017/V11/I3/429

Fig.1 (a) 3-D clustering and toroidal helix dataset for manifold learning; Mapping output of ICA (b), LPP (c) and MSAGL (d)

Fig.2 Total posterior log-likelihood of synthetic process

Fig.3 Monitoring performance of illustrative synthetic example by MSAGL, PCA and LPP based mixture model

Tab.1 Three benchmark scenario of the TEP

Fig.4 D statistics monitoring performance of Case 1 in TEP by MSAGL, PCA and LPP based mixture model

Fig.5 D (I²) statistics monitoring performance of Case 2 in TEP by MSAGL, PCA and LPP based mixture model

Fig.6 D (I²) statistics monitoring performance of Case 3 in TEP by MSAGL, PCA and LPP based mixture model

Tab.2 Comparison of fault detection (%) rates in three predefined cases of TEP

Tab.3 Comparison of false alarm (%) rates in three predefined cases of TEP

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